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Adaptive Model (adaptive + model)

Distribution by Scientific Domains

Business, Economics, Finance and Accounting	50%

Selected Abstracts

Modeling Longitudinal Spatial Periodontal Data: A Spatially Adaptive Model with Tools for Specifying Priors and Checking Fit

BIOMETRICS, Issue 3 2008
Brian J. Reich
Summary Attachment loss (AL), the distance down a tooth's root that is no longer attached to surrounding bone by periodontal ligament, is a common measure of periodontal disease. In this article, we develop a spatiotemporal model to monitor the progression of AL. Our model is an extension of the conditionally autoregressive (CAR) prior, which spatially smooths estimates toward their neighbors. However, because AL often exhibits a burst of large values in space and time, we develop a nonstationary spatiotemporal CAR model that allows the degree of spatial and temporal smoothing to vary in different regions of the mouth. To do this, we assign each AL measurement site its own set of variance parameters and spatially smooth the variances with spatial priors. We propose a heuristic to measure the complexity of the site-specific variances, and use it to select priors that ensure parameters in the model are well identified. In data from a clinical trial, this model improves the fit compared to the usual dynamic CAR model for 90 of 99 patients' AL measurements. [source]

An adaptive clinical Type 1 diabetes control protocol to optimize conventional self-monitoring blood glucose and multiple daily-injection therapy

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 5 2009
Xing-Wei Wong
Abstract The objective of this study was to develop a safe, robust and effective protocol for the clinical control of Type 1 diabetes using conventional self-monitoring blood glucose (SMBG) measurements, and multiple daily injection (MDI) with insulin analogues. A virtual patient method is used to develop an in silico simulation tool for Type 1 diabetes using data from a Type 1 diabetes patient cohort (n=40) . The tool is used to test two prandial insulin protocols, an adaptive protocol (AC) and a conventional intensive insulin therapy (IIT) protocol (CC) against results from a representative control cohort as a function of SMBG frequency. With the prandial protocols, optimal and suboptimal basal insulin replacement using a clinically validated, forced-titration regimen is also evaluated. A Monte Carlo (MC) analysis using variability and error distributions derived from the clinical and physiological literature is used to test efficacy and robustness. MC analysis is performed for over 1 400 000 simulated patient hours. All results are compared with control data from which the virtual patients were derived. In conditions of suboptimal basal insulin replacement, the AC protocol significantly decreases HbA1c for SMBG frequencies ,6/day compared with controls and the CC protocol. With optimal basal insulin, mild and severe hypoglycaemia is reduced by 86,100% over controls for all SMBG frequencies. Control with the CC protocol and suboptimal basal insulin replacement saturates at an SMBG frequency of 6/day. The forced-titration regimen requires a minimum SMBG frequency of 6/day to prevent increased hypoglycaemia. Overaggressive basal dose titration with the CC protocol at lower SMBG frequencies is likely caused by uncorrected postprandial hyperglycaemia from the previous night. From the MC analysis, a defined peak in control is achieved at an SMBG frequency of 8/day. However, 90% of the cohort meets American Diabetes Association recommended HbA1c with just 2 measurements a day. A further 7.5% requires 4 measurements a day and only 2.5% (1 patient) required 6 measurements a day. In safety, the AC protocol is the most robust to applied MC error. Over all SMBG frequencies, the median for severe hypoglycaemia increases from 0 to 0.12% and for mild hypoglycaemia by 0,5.19% compared with the unrealistic no error simulation. While statistically significant, these figures are still very low and the distributions are well below those of the controls group. An adaptive control protocol for Type 1 diabetes is tested in silico under conditions of realistic variability and error. The adaptive (AC) protocol is effective and safe compared with conventional IIT (CC) and controls. As the fear of hypoglycaemia is a large psychological barrier to appropriate glycaemic control, adaptive model-based protocols may represent the next evolution of IIT to deliver increased glycaemic control with increased safety over conventional methods, while still utilizing the most commonly used forms of intervention (SMBG and MDI). The use of MC methods to evaluate them provides a relevant robustness test that is not considered in the no error analyses of most other studies. Copyright © 2008 John Wiley & Sons, Ltd. [source]

International Strategic Human Resource Management: A Comparative Case Analysis of Spanish Firms in China

MANAGEMENT AND ORGANIZATION REVIEW, Issue 2 2009
Yingying Zhang
abstract This study examines the role of human resources in strategy formulation processes in China's emerging market. Employing a qualitative data driven thematic analysis, we present evidence collected from six comparative case sites of Spanish firms in China. Our findings suggest that high performing firms use a dynamic adaptive logic while lower performing firms use a static structural logic. A dynamic adaptive model of strategic human resource management is identified, emphasizing a fluid and informal process between strategy, human resources and international management. [source]

LOCALIZED TECHNICAL PROGRESS AND CHOICE OF TECHNIQUE IN A LINEAR PRODUCTION MODEL

METROECONOMICA, Issue 2 2005
Antonio D'Agata
ABSTRACT The problem of choice of technique in single production linear models has been extensively analysed under the assumption that the set of processes available in the economy is exogenously given and globally known. However, since Atkinson and Stiglitz's 1969 article economists have considered technical change as a cumulative, localized and adaptive process. The aim of this paper is to develop an adaptive model of choice of technique within a classical theoretical framework. Our model provides, although in a very stylized way, an explicit description of the relationship between the currently employed processes of production and the new ones. This allows us to analyse in a rigorous way the ,secular' dynamics of the economy. [source]